1. Field of the Invention
The present invention is a method for mapping and identifying entity information in a system that includes a database.
2. Description of the Related Art
In the world of information technology, great consequence rests on the ability to uniquely identify a data entity within the context of a population of data. Whether this data exists within the realm of marketing, finance, education, medicine or some other field, the concept of an identity represented by data elements is fundamental to the vast majority of applications upon which computing power is applied. At the intersection of commerce, global population growth, health care and privacy issues, there is a basic need for the demonstrated ability to distinguish between one entity and another with reliable and predictable results despite shifting data values.
These entity matching tasks are often completed with a master patient index (MPI) and associated applications. An MPI is well known and utilized in the healthcare industry. As healthcare systems become increasingly complex and distributed over wide areas, it is important to be able to uniquely and correctly identify individual patients over a wide array of disjoint or unconnected systems.
Medsphere, QuadraMed and IBM all have developed MPI systems. Current MPI systems seek to uniquely identify an individual based on information provided. Often this information centers on demographic data, and can be sparse or outdated, which can lead to the discontinuity or loss of important patient data. Current MPI systems take information provided and perform pattern-matching comparisons with persons already known to the system. The pattern-matching algorithms employed make probabilistic determinations based on the relevance of certain data points or attributes. Some attributes are more valuable than others in determining a match, and so potential matches on highly relevant attributes necessarily increase the likelihood of a correct match. Once an MPI system is consulted and a match is made, previously stored patient information are retrieved with confidence that it is the proper information for the patient in question.
Pattern Matching algorithm for Symptom-Disease Matching Incorporated by Reference
Pattern matching algorithms are used to match two sets of data. For example a multi-membership Bayesian algorithm can be implemented to match a set of medical symptoms to a disease. The multi-membership Bayesian algorithm to perform this symptom-disease matching is disclosed by “A Feature Dictionary To Support Database Translation, Information Retreival, Intelligent Medical Records, and Expert Systems” by Frank Fariborz Naeymi-Rad, and is incorporated by reference as if fully reproduced herein.
An efficient and accurate MPI system should identify a match or a lack of a match with a large enough confidence as to eliminate the need for human intervention. Current MPI systems do not use adequate pattern-matching algorithms, as there is still need for extensive human intervention. As a system stores patient information, the confidence of a positive or negative match increases. Current MPI systems do not increase confidence fast enough and therefore are inefficient. Also, because the confidence of matching is not high, inaccurate results occur. Therefore it is desired that an efficient and accurate MPI system and pattern-matching algorithm be developed.
Furthermore, current MPI systems are geared exclusively toward the healthcare industry. Due to this, the attributes used to describe the entities are specific to healthcare related attributes. Therefore, current MPI systems cannot be used by other industries that seek to identify a data entity represented by data elements.